Hi,
yes, initially we thought about introducing a LabeledVector where the label
can be a vector. However, for the sake of simplicity we decided to first
implement a LabeledVector with a single double value as label.
A simple double value should take 8 bytes of memory space. The
DenseVector(Array(
Hi Theodore,
Thanks for explaining the reason. :)
So how about change LabeledVector contains two vectors? One of vectors is for
label and the other one is for value. I think this approach would be okay
because a double value label could be represented as a
DenseVector(Array(LABEL_VALUE)).
Onl
Generalizing the type of the label for the label vector is an idea we
played with when designing the current optimization framework.
We ended up deciding against it as the double type allows us to do
regressions and (multiclass) classification which should be the majority of
the use cases out ther
Hi,
yes it is a good idea. One implementaiton with a single valued label and
a second implementation with a label vector.
Best Regards,
Hilmi
From: *Chiwan Park* mailto:chiwanp...@apache.org>>
Date: Tue, Jan 5, 2016 at 12:17 PM
Subject: Re: LabeledVector with label vector
T
Hi Hilmi,
Thanks for suggestion about type of labeled vector. Basically, I agree that
your suggestion is reasonable. But, I would like to generialize `LabeledVector`
like following example:
```
case class LabeledVector[T <: Serializable](label: T, vector: Vector) extends
Serializable {
// so
Hi,
in the ML-Pipeline of Flink we have the "LabeledVector" class. It
consists of a vector and a label as a double value. Unfortunately, it is
not applicable for sequence learning where the label is also a vector.
For example, in NLP we have a vector of words and the label is a vector
of the c
